The Probit Choice Model Under Sequential Search with an Application to Online Retailing
The Probit Choice Model Under Sequential Search with an Application to Online Retailing
复制标题
DOI：
10.1287/mnsc.2016.2545
复制
发表时间：
2017-11-01
影响因子：
5.4
通讯作者：
Bronnenberg, Bart J.
中科院分区：
文献类型：
--
作者：
Kim, Jun B.;Albuquerque, Paulo;Bronnenberg, Bart J.
建立了最优序贯搜索下的概率单位选择模型，并将其应用于耐用消费品总需求的研究。在我们的搜索和选择的联合模型中，我们推导出一个表达式的概率选择，服从最优顺序搜索所施加的全部限制。我们的部分分析模型的估计避免了在选择概率的评估中计算高维积分，这在搜索集很大时特别有益。我们展示了我们的方法在数据实验中的优势，并将该模型应用于Amazon.com摄像机产品类别的聚合搜索和选择数据。我们发现，搜索和选择数据的联合使用提供了更好的性能，在推理和预测方面，比单独使用搜索数据，并导致消费者替代模式的现实估计。
We develop a probit choice model under optimal sequential search and apply it to the study of aggregate demand of consumer durable goods. In our joint model of search and choice, we derive an expression for the probability of choice that obeys the full set of restrictions imposed by optimal sequential search. Estimation of our partially analytic model avoids the computation of high-dimensional integrations in the evaluation of choice probabilities, which is of particular benefit when search sets are large. We demonstrate the advantages of our approach in data experiments and apply the model to aggregate search and choice data from the camcorder product category at Amazon.com. We show that the joint use of search and choice data provides better performance in terms of inferences and predictions than using search data alone and leads to realistic estimates of consumer substitution patterns.